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Knowledge graphs have shown promise for several cybersecurity tasks, such asvulnerability assessment and threat analysis. In this work, we present a newmethod for constructing a vulnerability knowledge graph from information in theNational Vulnerability Database (NVD). Our approach combines named entityrecognition (NER), relation extraction (RE), and entity prediction using acombination of neural models, heuristic rules, and knowledge graph embeddings.We demonstrate how our method helps to fix missing entities in knowledge graphsused for cybersecurity and evaluate the performance.

Constructing a Knowledge Graph from Textual Descriptions of Software Vulnerabilities in the National Vulnerability Database